Language distinguishes humans from all other animals and is strongly related to intelligence. Improving language ability typically results in a higher intelligent quotient (IQ) as well as improved literacy and academic skills. A child's language ability and vocabulary at age three is a strong predictor of both intelligence and test scores in reading and math at age ten and beyond.
Children begin to acquire language at birth. The early years (i.e., birth to age four) are critical for language development. Though humans learn vocabulary and language throughout their lives, these early years establish a trajectory for later language development.
Humans are natural language learners. The ability to learn language is genetically programmed in the human species. Early language ability develops in natural contexts instinctively as an outgrowth of the conversations between a child and his or her parent or primary caregiver. Early language ability develops from many social interactions including when a parent reads a book to a child. Television and computers can also result in language learning, although they are not typically major contributors.
A rich aural or listening language environment in which many words are spoken with a high number of affirmations versus prohibitions produces children who have high language ability and higher than normal IQ. Even after children begin school, and after children begin to read, much of our language ability and vocabulary, the words we know (receptive vocabulary) and the words we use in speech (expressive vocabulary) come incidentally from conversation with people around us. While some vocabulary is often learned formally in school through studying lists of vocabulary words or from computer software programs designed to teach vocabulary and informally through book reading, the foundation of human language ability and vocabulary comes from social interaction, conversation, and listening to others speak.
Not only does a child's language ability develop from hearing others speak and speaking to them (i.e., turn-taking), the child's own speech is a dynamic indicator of cognitive functioning. Research techniques have been developed which involve counting a young child's vocalizations and utterances to estimate a child's cognitive development. However, the current process of collecting this information requires human observers which is obtrusive and influences behavior. It additionally requires transcription of audio recordings which is expensive and time consuming.
Much of what we know about how language develops in children comes from research studies in which parent and child speech were recorded in a natural home environment. Once recorded, the speech was manually transcribed to create text files. From these text files, various metrics were derived such as number of phonemes, morphemes, utterances, words, nouns, verbs, modifiers, declarations, interrogatives, imperatives, affirmatives, prohibitions, sentences and phrases. These and other metrics or combinations and transformations thereof of parent speech were then related to measures of the child's language ability, vocabulary size, IQ, literacy and other academic skills to show their causative relationship. An example of such a research study is described in Hart and Risley, “Meaningful Differences in the Everyday Experiences of Young American Children,” 1995.
The type of study such as undertaken by Hart and Risley is difficult and expensive to perform because the process of first recording, then converting speech to text and coding text using human observers and transcribers is very laborious. A need exists for systems and methods that reduce the time and cost of this type of data gathering and analysis. By reducing these costs, it becomes possible to perform studies more easily and with vastly larger data sets. Moreover, there also is a need for systems and methods that feedback the speech environment information and estimates of a child's linguistic and cognitive functioning to speakers in homes, day care centers, classrooms, businesses, and other contexts to enable users to enhance learning and development in children, students, and potentially adult learners who may be deficient in language development or are learning a second language.
Even in the classroom, an educator may be teaching one subject while indirectly undermining another subject. For example, an educator may be conscious of using sophisticated vocabulary during language arts courses, but revert to more rudimentary vocabulary during mathematics, fine art, physical education, or other courses where vocabulary is not of primary concern to the curriculum goals. At best, these situations fail to take advantage of an available learning opportunity by integrating vocabulary education with other topics. At worst, these situations may actually undermine the language arts learning that was presented directly in other courses.
Conventional vocabulary education often involves presenting words (verbally and/or textually) to a student along with an image, sound, or other stimulus that represents the meaning of the particular word being taught. The presentation may occur in books, by a teacher, using software, or other means. While potentially effective in the short term, these types of activities do not occur in “real world” contexts, and so this type of education is rarely repeated or reinforced outside of the classroom.
A variety of games have been developed for home and classroom use that attempt to embed the process of pairing the presentation of words and meaningful images in the context of a game. These efforts have some positive effect because they make vocabulary education more engaging, and they encourage vocabulary usage outside of the classroom environment. However, these game-type approaches generally create an artificial context for vocabulary training, and so do not take advantage of the large amount of language learning that can occur in the context of day-to-day activities.
Accordingly, there remains a need for systems and methods for automatically monitoring vocabulary and language usage in the context of day-to-day activities, developing metrics indicating characteristics of contextual language usage, and reporting those metrics to speakers so that they may alter their speech and verbal interactions in a manner that supports vocabulary and language improvement and thus influences IQ and academic success.
As discussed in more detail herein, the language environment surrounding a young child is key to the child's development. A child's language and vocabulary ability at age three, for example, can indicate intelligence and test scores in academic subjects such as reading and math at later ages. Improving language ability typically results in a higher intelligent quotient (IQ) as well as improved literacy and academic skills.
Exposure to a rich aural or listening language environment in which many words are spoken with a large number of interactive conversational turns between the child and adult and a relatively high number of affirmations versus prohibitions may promote an increase in the child's language ability and IQ. The effect of a language environment surrounding a child of a young age on the child's language ability and IQ may be particularly pronounced.
In the first four years of human life, a child experiences a highly intensive period of speech and language development due in part to the development and maturing of the child's brain. Even after children begin attending school or reading, much of the child's language ability and vocabulary, including the words known (receptive vocabulary) and the words the child uses in speech (expressive vocabulary), are developed from conversations the child experiences with other people.
In addition to hearing others speak to them and responding (i.e., conversational turns), a child's language development may be promoted by the child's own speech. The child's own speech is a dynamic indicator of cognitive functioning, particularly in the early years of a child's life. Research techniques have been developed which involve counting a young child's vocalizations and utterances and length of utterances to estimate a child's cognitive development. Current processes of collecting information may include obtaining data via a human observer and/or a transcription of an audio recording of the child's speech. The data is analyzed to provide metrics with which the child's language environment can be analyzed and potentially modified to promote increasing the child's language development and IQ.
The presence of a human observer, however, may be intrusive, influential on the child's performance, costly, and unable to adequately obtain information on a child's natural environment and development. Furthermore, the use of audio recordings and transcriptions is a costly and time-consuming process of obtaining data associated with a child's language environment. The analysis of such data to identify canonical babbling, count the number of words, determine mean length of utterances and other vocalization metrics, and determine content spoken is also time intensive.
Counting the number of words and determining content spoken may be particularly time and resource intensive, even for electronic analysis systems, since each word is identified along with its meaning. Accordingly, a need exists for methods and systems for obtaining and analyzing data associated with a child's language environment independent of content and reporting metrics based on the data in a timely manner. The analysis should also include an automatic assessment of the child's expressive language development.
Beyond an automatic assessment of a child's expressive language development, a need exists for the development of specific metrics and methodologies for determining specific developmental disorders in a child. As expressed above, a test that is largely non-invasive, in terms of providing a human observer, and that is of low cost while at the same time generating a substantial amount of data is desirable. One such developmental disorder of interest that can be detected through the analysis of speech is autism. Another factor contributing to language development may be emotion. When children are exposed to an emotionally stressed environment there learning and language development may suffer. Therefore, a system and method for detecting the emotional content of subject interactions may be desirable for assisting in language development.